공간분석을 활용한 식 행동 및 관련요인을 이용한 예측분석: 2016 지역사회건강조사자료

Abstract

This study was carried out to investigate the magnitude and pattern of small area variations as defined by Si-gun-gu and to predict factors related to eating behavior and chronic disease using the 2016 Community Health Survey (CHS) data. The subjects of the analysis were the population of 254 counties surveyed in the 2009 CHS. The magnitude and pattern of area variations in the given eating behavior and chronic diseases were examined using Extremal Quotient (EQ) and Coefficients of Variation (CV) of Small Area Variation Analysis (SAVA). A spatial autocorrelation regression model was used to examine the related factors with these variations. Assessment of breakfast eating frequency in each community indicated that the lowest eating frequency per week was 4.624 while the highest eating frequency was 6.739. People in urban areas had a lower number of breakfast eating days than those in rural areas. When a spatial analysis of general characteristics and breakfast eating frequency was conducted, it was predicted that the people in the areas with a higher female population, age, and income level would have a higher number of breakfast eating days. As for the correlation between chronic disease diagnosis and breakfast eating days, it was predicted that those who were diagnosed with diabetes would eat breakfast. The analysis of general characteristics and usual salt consumption levels predicted that the people in areas with a higher male population, age, and income level would consume less salt. As for the correlation between chronic diseases and salt consumption levels, it was predicted that those who were never diagnosed with hypertension would take less salt. These results will help us in the development of policies for population-based health promotion through a reduction of the gap in eating behavior indices between areas. Further research is needed to build accurate and reliable models of CHS data.